From 9d3b2bb310df220dc3801c53f9faad3a21b17883 Mon Sep 17 00:00:00 2001 From: jim-sheldon <5619539+jim-sheldon@users.noreply.github.com> Date: Mon, 29 Apr 2024 20:51:51 -0400 Subject: [PATCH] Refactor clinicaltrials.gov processor --- .github/workflows/stability_tests.yml | 31 + LICENSE | 25 + README.md | 64 + composition.svg | 1 + poetry.lock | 2069 +++++++++++++++++ pyproject.toml | 37 + .../clinical_trials_dot_gov/__main__.py | 45 + .../clinical_trials_dot_gov/config.py | 76 + .../clinical_trials_dot_gov/fetch.py | 167 ++ .../clinical_trials_dot_gov/process.py | 79 + .../rest_api_response_models.py | 107 + .../clinical_trials_dot_gov/store.py | 168 ++ .../clinical_trials_dot_gov/transform.py | 297 +++ .../clinical_trials_dot_gov/util.py | 16 + .../clinical_trials_dot_gov/validate.py | 413 ++++ tests/test_clinical_trials_dot_gov.py | 29 + 16 files changed, 3624 insertions(+) create mode 100644 .github/workflows/stability_tests.yml create mode 100644 LICENSE create mode 100644 README.md create mode 100644 composition.svg create mode 100644 poetry.lock create mode 100644 pyproject.toml create mode 100644 src/trial_synth/clinical_trials_dot_gov/__main__.py create mode 100644 src/trial_synth/clinical_trials_dot_gov/config.py create mode 100644 src/trial_synth/clinical_trials_dot_gov/fetch.py create mode 100644 src/trial_synth/clinical_trials_dot_gov/process.py create mode 100644 src/trial_synth/clinical_trials_dot_gov/rest_api_response_models.py create mode 100644 src/trial_synth/clinical_trials_dot_gov/store.py create mode 100644 src/trial_synth/clinical_trials_dot_gov/transform.py create mode 100644 src/trial_synth/clinical_trials_dot_gov/util.py create mode 100644 src/trial_synth/clinical_trials_dot_gov/validate.py create mode 100644 tests/test_clinical_trials_dot_gov.py diff --git a/.github/workflows/stability_tests.yml b/.github/workflows/stability_tests.yml new file mode 100644 index 0000000..3168f79 --- /dev/null +++ b/.github/workflows/stability_tests.yml @@ -0,0 +1,31 @@ +name: Test G.h subsystem + +on: + push: + branches: [main] + paths: + - ".github/workflows/stability_tests.yml" + - "src/*" + pull_request: + paths: + - ".github/workflows/stability_tests.yml" + - "src/*" + workflow_dispatch: + +jobs: + tests: + runs-on: ubuntu-22.04 + steps: + - uses: actions/checkout@v3 + + - name: Install Cython dependencies + run: sudo apt update && sudo apt install -y gcc build-essential python3-dev curl + + - name: Install poetry + run: curl -sSL https://install.python-poetry.org | python3 - + + - name: Install python dependencies + run: poetry install + + - name: Run tests + run: poetry run pytest -v diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..73e2590 --- /dev/null +++ b/LICENSE @@ -0,0 +1,25 @@ +BSD 2-Clause License + +Copyright (c) 2021, Benjamin M. Gyori +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/README.md b/README.md new file mode 100644 index 0000000..b83d298 --- /dev/null +++ b/README.md @@ -0,0 +1,64 @@ +# Trial Synth + +Interpretation and integration of clinical trials data from multiple registries in a computable form + + +## Adding registries and processors + +Getting data of each registry happens via a corresponding `Processor` object, its components, and relevant methods and functions. +Users create a `Processor` by composition; some require different work than others. + +Some common components include: +- Fetcher: get raw data from a source (e.g. a REST API) +- Transformer: change the data into a desired format for downstream use +- Storer: save the data to some location(s) +- Validator: check the data for quality +- Configuration: management of processor behaviors +- CLI: entrypoint for downstream use + +![Processor with common components](./composition.svg) + +A user can add a registry and `Processor` by test-driven development. A pattern for doing this looks like: +- Get a sample response from the API and save it to a file +- Write the test for API stability + - This can detect unexpected API behavior (e.g. changes or outages) +- Add the file to the imposter to use as a stubbed response + - Add a new port for the stubbed service to the imposter and compose files +- Write the test for the processor's end-to-end behaviors + - The input is the sample response +- Write the components and functions to make the test pass + - Set the `url` environment variable for the `Processor` to the stub's port + + +## Installation + +Run the following to install this repository's package in editable mode: + +``` +$ git clone https://github.com/gyorilab/trialsynth +$ cd trialsynth +$ pip install -e +``` + + +## Local run + +Users can run all system components on their computers via the compose stack, wrapped by a shell script: +``` +./run_e2e.sh +``` + + +## Testing + +Users can run all the suite of full system tests on their computers via the compose stack, wrapped by a shell script: +``` +./test_e2e.sh +``` + + +## References + +[Test-driven development](https://tidyfirst.substack.com/p/canon-tdd) +[Mountebank](https://www.mbtest.org/) + diff --git a/composition.svg b/composition.svg new file mode 100644 index 0000000..441efbb --- /dev/null +++ b/composition.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..9cec56b --- /dev/null +++ b/poetry.lock @@ -0,0 +1,2069 @@ +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. + +[[package]] +name = "addict" +version = "2.4.0" +description = "Addict is a dictionary whose items can be set using both attribute and item syntax." +optional = false +python-versions = "*" +files = [ + {file = "addict-2.4.0-py3-none-any.whl", hash = "sha256:249bb56bbfd3cdc2a004ea0ff4c2b6ddc84d53bc2194761636eb314d5cfa5dfc"}, + {file = "addict-2.4.0.tar.gz", hash = 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+ "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", +] + +[tool.poetry.dependencies] +python = ">=3.10" +gilda = "^1.1.0" +pandas = "<2" # indra does not support >=2 +tqdm = "^4.66.2" +indra = {git = "https://github.com/sorgerlab/indra.git"} +requests = "^2.31.0" +click = "^8.1.7" +addict = "^2.4.0" +pydantic = "^2.7.1" + +[tool.poetry.group.test.dependencies] +pytest = "^8.1.1" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + diff --git a/src/trial_synth/clinical_trials_dot_gov/__main__.py b/src/trial_synth/clinical_trials_dot_gov/__main__.py new file mode 100644 index 0000000..21b39dd --- /dev/null +++ b/src/trial_synth/clinical_trials_dot_gov/__main__.py @@ -0,0 +1,45 @@ +import logging + +import click + +from .config import Config, DATA_DIR +from .fetch import Fetcher +from .store import Storer +from .transform import Transformer +from .process import Processor +from .util import ensure_output_directory_exists + + +logger = logging.getLogger(__name__) + + +@click.command() +def main(): + click.secho("Processing clincaltrials.gov data", fg="green", bold=True) + ensure_output_directory_exists() + config = Config() + fetcher = Fetcher( + url=config.api_url, + request_parameters=config.api_parameters + ) + transformer = Transformer() + storer = Storer( + node_iterator=transformer.get_nodes, + node_types=config.node_types, + data_directory=DATA_DIR + ) + clinical_trials_processor = Processor( + config=config, + fetcher=fetcher, + storer=storer, + transformer=transformer + ) + clinical_trials_processor.ensure_api_response_data_saved() + clinical_trials_processor.clean_and_transform_data() + clinical_trials_processor.set_nodes_and_edges() + clinical_trials_processor.validate_data() + clinical_trials_processor.save_graph_data() + + +if __name__ == "__main__": + main() diff --git a/src/trial_synth/clinical_trials_dot_gov/config.py b/src/trial_synth/clinical_trials_dot_gov/config.py new file mode 100644 index 0000000..f1bebb1 --- /dev/null +++ b/src/trial_synth/clinical_trials_dot_gov/config.py @@ -0,0 +1,76 @@ +"""Clinicaltrials.gov processor configuration""" + +from dataclasses import dataclass +import logging +import os +from pathlib import Path + + +LOGGING_LEVEL = os.environ.get("LOGGING_LEVEL", "INFO") + +PROCESSOR_NAME = os.environ.get( + "CLINICAL_TRIALS_DOT_GOV_PROCESSOR_NAME", + "clinicaltrials" +) +API_URL = os.environ.get( + "CLINICAL_TRIALS_DOT_GOV_API_URL", + "https://clinicaltrials.gov/api/v2/studies" +) + +HOME_DIR = os.environ.get("HOME_DIRECTORY", Path.home()) +PARENT_DIR_STR = os.environ.get("BASE_DIRECTORY", ".data") +DATA_DIR_STR = os.environ.get("DATA_DIRECTORY", "clinicaltrials") +DATA_DIR = Path(HOME_DIR, PARENT_DIR_STR, DATA_DIR_STR) +UNPROCESSED_FILE_PATH_STR = os.environ.get( + "CLINICAL_TRIALS_RAW_DATA", + "clinical_trials.tsv.gz" +) +NODES_FILE_NAME_STR = os.environ.get("NODES_FILE", "nodes.tsv.gz") +NODES_INDRA_FILE_NAME_STR = os.environ.get("NODES_INDRA_FILE", "nodes.pkl") +EDGES_FILE_NAME_STR = os.environ.get("EDGES_FILE", "edges.tsv.gz") + +FIELDS = [ + "NCTId", + "BriefTitle", + "Condition", + "ConditionMeshTerm", + "ConditionMeshId", + "InterventionName", + "InterventionType", + "InterventionMeshTerm", + "InterventionMeshId", + "StudyType", + "DesignAllocation", + "OverallStatus", + "Phase", + "WhyStopped", + "SecondaryIdType", + "SecondaryId", + "StartDate", # Month [day], year: "November 1, 2023", "May 1984" or NaN + "StartDateType", # "Actual" or "Anticipated" (or NaN) + "ReferencePMID" # these are tagged as relevant by the author, but not necessarily about the trial +] + +root = logging.getLogger() +root.setLevel(LOGGING_LEVEL) + + +@dataclass +class Config: + """ + User-mutable properties of Clinicaltrials.gov data processing + """ + + name = PROCESSOR_NAME + api_url = API_URL + api_parameters = { + "fields": ",".join(FIELDS), # actually column names, not fields + "pageSize": 1000, + "countTotal": "true" + } + + unprocessed_file_path = Path(DATA_DIR, UNPROCESSED_FILE_PATH_STR) + nodes_path = Path(DATA_DIR, NODES_FILE_NAME_STR) + nodes_indra_path = Path(DATA_DIR, NODES_INDRA_FILE_NAME_STR) + edges_path = Path(DATA_DIR, EDGES_FILE_NAME_STR) + node_types = ["BioEntity", "ClinicalTrial"] diff --git a/src/trial_synth/clinical_trials_dot_gov/fetch.py b/src/trial_synth/clinical_trials_dot_gov/fetch.py new file mode 100644 index 0000000..b0c3e6d --- /dev/null +++ b/src/trial_synth/clinical_trials_dot_gov/fetch.py @@ -0,0 +1,167 @@ +"""Gets Clinicaltrials.gov data from REST API or saved file""" + +import logging +from pathlib import Path + +import pandas as pd +import requests +from tqdm import trange + +from .config import FIELDS +from .rest_api_response_models import UnflattenedTrial + + +TRIAL = {field: None for field in FIELDS} + +logger = logging.getLogger(__name__) + + +class Fetcher: + + def __init__(self, url, request_parameters): + self.raw_data = pd.DataFrame() + self.url = url + self.request_parameters = request_parameters + self.total_pages = 0 + + def get_api_data(self, url: str, request_parameters: dict) -> None: + logger.debug(f"Fetching Clinicaltrials.gov data from {url} with parameters {request_parameters}") + + try: + self.read_next_page() + # start on page "1" because we already did page 0 above. Note that we're zero-indexed, + # so "1" is actually is the second page + pages = 1 + self.total_pages + for page in trange(1, pages, unit="page", desc="Downloading ClinicalTrials.gov"): + self.read_next_page() + + except Exception: + logger.exception(f"Could not fetch data from {url}") + raise + + def read_next_page(self) -> None: + json_data = send_request(self.url, self.request_parameters) + studies = json_data.get("studies", []) + flattened_data = self.flatten_data(studies) + self.raw_data = pd.concat([self.raw_data, flattened_data]) + next_page_token = json_data.get("nextPageToken") + self.request_parameters["pageToken"] = next_page_token + if not self.total_pages: + self.total_pages = json_data.get("totalCount") // self.request_parameters.get("pageSize") + + +def flatten_data(data: dict) -> pd.DataFrame: + logger.debug("Reformatting API response data from hierarchical to tabular") + + trials = [] + + for trial_data in data: + unflattened_trial = UnflattenedTrial(**trial_data) + + condition_browse_module = unflattened_trial.derived_section.condition_browse_module + condition_meshes = condition_browse_module.condition_meshes + + condition_mesh_terms = [mesh.term for mesh in condition_meshes] + condition_mesh_ids = [mesh.mesh_id for mesh in condition_meshes] + + arms_interventions_module = unflattened_trial.protocol_section.arms_interventions_module + arms_interventions = arms_interventions_module.arms_interventions + + intervention_names = [ + i.name for i in arms_interventions + ] + intervention_types = [ + i.intervention_type.capitalize() for i in arms_interventions + ] + + intervention_browse_module = unflattened_trial.derived_section.intervention_browse_module + intervention_meshes = intervention_browse_module.intervention_meshes + intervention_mesh_terms = [mesh.term for mesh in intervention_meshes] + intervention_mesh_ids = [mesh.mesh_id for mesh in intervention_meshes] + + secondary_info = unflattened_trial.protocol_section.id_module.secondary_ids + + secondary_id_types = [s.id_type for s in secondary_info] + secondary_ids = [s.secondary_id for s in secondary_info] + + reference_pmids = [r.pmid for r in unflattened_trial.protocol_section.id_module.secondary_ids] # these are tagged as relevant by the author, but not necessarily about the trial + + conditions_str = join_if_not_empty(unflattened_trial.protocol_section.conditions_module.conditions) + condition_mesh_terms_str = join_if_not_empty(condition_mesh_terms) + condition_mesh_ids_str = join_if_not_empty(condition_mesh_ids) + intervention_names_str = join_if_not_empty(intervention_names) + intervention_types_str = join_if_not_empty(intervention_types) + intervention_mesh_terms_str = join_if_not_empty(intervention_mesh_terms) + intervention_mesh_ids_str = join_if_not_empty(intervention_mesh_ids) + phases_str = join_if_not_empty(unflattened_trial.protocol_section.design_module.phases) + if phases_str: + phases_str = phases_str.replace("PHASE", "Phase ").replace("NA", "Not Applicable") + secondary_id_types_str = join_if_not_empty(secondary_id_types) + secondary_ids_str = join_if_not_empty(secondary_ids) + reference_pmids_str = join_if_not_empty(reference_pmids) + + design_module = unflattened_trial.protocol_section.design_module + + design_allocation_str = design_module.design_info.allocation + if design_allocation_str: + design_allocation_str = design_allocation_str.replace("NA", "N/A").replace("NON_RANDOMIZED", "Non-Randomized").replace("RANDOMIZED", "Randomized") + + trial = TRIAL.copy() + + id_module = unflattened_trial.protocol_section.id_module + status_module = unflattened_trial.protocol_section.status_module + start_date_struct = status_module.start_date_struct + + trial["NCTId"] = id_module.nct_id + trial["BriefTitle"] = id_module.brief_title + trial["Condition"] = conditions_str + trial["ConditionMeshTerm"] = condition_mesh_terms_str + trial["ConditionMeshId"] = condition_mesh_ids_str + trial["InterventionName"] = intervention_names_str + trial["InterventionType"] = intervention_types_str + trial["InterventionMeshTerm"] = intervention_mesh_terms_str + trial["InterventionMeshId"] = intervention_mesh_ids_str + trial["StudyType"] = design_module.study_type + if trial["StudyType"]: + trial["StudyType"] = trial["StudyType"].capitalize() + trial["DesignAllocation"] = design_allocation_str + trial["OverallStatus"] = status_module.overall_status + if trial["OverallStatus"]: + trial["OverallStatus"] = trial["OverallStatus"].capitalize() + trial["Phase"] = phases_str + trial["WhyStopped"] = status_module.why_stopped + if trial["WhyStopped"]: + trial["WhyStopped"] = trial["WhyStopped"].capitalize() + trial["SecondaryIdType"] = secondary_id_types_str + trial["SecondaryId"] = secondary_ids_str + trial["StartDate"] = start_date_struct.date # Month [day], year: "November 1, 2023", "May 1984" or NaN + trial["StartDateType"] = start_date_struct.date_type # "Actual" or "Anticipated" (or NaN) + trial["ReferencePMID"] = reference_pmids_str + + trials.append(trial) + + return pd.DataFrame.from_dict(trials) + + +def load_saved_data(path: Path) -> pd.DataFrame: + logger.debug(f"Loading Clinicaltrials.gov data from {path}") + + try: + return pd.read_csv(path, sep="\t") + except Exception: + logger.exception(f"Could not load data from {path}") + + +def send_request(url: str, params: dict) -> dict: + try: + response = requests.get(url, params) + return response.json() + except Exception: + logger.exception(f"Error with request to {url} using params {params}") + raise + + +def join_if_not_empty(data: list, delimiter: str = "|") -> str | None: + if all(data): + return delimiter.join(data) + return None diff --git a/src/trial_synth/clinical_trials_dot_gov/process.py b/src/trial_synth/clinical_trials_dot_gov/process.py new file mode 100644 index 0000000..eb0618f --- /dev/null +++ b/src/trial_synth/clinical_trials_dot_gov/process.py @@ -0,0 +1,79 @@ +from collections import defaultdict +import logging + +from tqdm import tqdm + +from .config import Config +from .fetch import Fetcher, load_saved_data +from .store import Storer +from .transform import Transformer +from .validate import validate_node_data, validate_edge_data + + +logger = logging.getLogger(__name__) + + +class Processor: + + def __init__( + self, + config: Config, + fetcher: Fetcher, + storer: Storer, + transformer: Transformer, + ): + self.config = config + self.fetcher = fetcher + self.storer = storer + self.transformer = transformer + + self.node_iterator = self.transformer.get_nodes + self.nodes_by_type = defaultdict(list) + self.edges = set() + + def ensure_api_response_data_saved(self, refresh: bool = False) -> None: + logger.debug("Ensuring input data exists on disk") + if self.config.unprocessed_file_path.is_file() and not refresh: + self.fetcher.raw_data = load_saved_data(self.config.unprocessed_file_path) + else: + self.fetcher.get_api_data(self.config.api_url, self.config.api_parameters) + self.storer.save_data(self.fetcher.raw_data, self.config.unprocessed_file_path) + + def clean_and_transform_data(self) -> None: + logger.debug("Cleaning and transforming data") + self.transformer.df = self.fetcher.raw_data + self.transformer.clean_data_values() + + def set_nodes_and_edges(self) -> None: + self.set_nodes_by_type() + self.set_edges() + + def set_nodes_by_type(self) -> None: + logger.debug("Setting nodes by type") + nodes = tqdm( + self.node_iterator(), + desc="Node generation", + unit_scale=True, + unit="node" + ) + + for ix, node in enumerate(nodes): + self.nodes_by_type[node["labels"][0]].append(node) + + def set_edges(self) -> None: + logger.debug("Setting edges") + self.edges = list(self.transformer.get_edges()) + if len(self.edges) == 0: + raise RuntimeError(f"No relations were generated for {self.name}") + + def validate_data(self) -> None: + logger.debug("Validating graph data") + for nodes in self.nodes_by_type.values(): + validate_node_data(self.config.name, nodes) + validate_edge_data(self.config.name, self.edges) + + def save_graph_data(self) -> None: + logger.debug("Saving output data") + self.storer.save_node_data() + self.storer.save_edge_data(self.edges) + diff --git a/src/trial_synth/clinical_trials_dot_gov/rest_api_response_models.py b/src/trial_synth/clinical_trials_dot_gov/rest_api_response_models.py new file mode 100644 index 0000000..537af96 --- /dev/null +++ b/src/trial_synth/clinical_trials_dot_gov/rest_api_response_models.py @@ -0,0 +1,107 @@ +from pydantic import BaseModel, Field + + + +class SecondaryID(BaseModel): + + id_type: str = Field(alias="type") + secondary_id: str = Field(alias="id") + + +class IDModule(BaseModel): + + nct_id: str = Field(alias="nctId") + brief_title: str = Field(alias="briefTitle") + secondary_ids: list[SecondaryID] = Field(alias="secondaryIds", default=[]) + + +class ConditionsModule(BaseModel): + + conditions: list[str] = Field(default=[]) + + +class StartDateStruct(BaseModel): + + date: str = Field(default=None) + date_type: str = Field(alias="type", default=None) + + +class StatusModule(BaseModel): + + start_date_struct: StartDateStruct = Field(alias="startDateStruct", default=StartDateStruct()) + overall_status: str = Field(alias="overallStatus") + why_stopped: str = Field(alias="whyStopped", default=None) + + +class DesignInfo(BaseModel): + + allocation: str = Field(alias="allocation", default=None) + + +class DesignModule(BaseModel): + + study_type: str = Field(alias="studyType", default=None) + phases: list[str] = Field(alias="phases", default=[]) + design_info: DesignInfo = Field(alias="designInfo", default=DesignInfo()) + + +class Reference(BaseModel): + + pmid: str # these are tagged as relevant by the author, but not necessarily about the trial + + +class ReferencesModule(BaseModel): + + references: list[Reference | dict] = Field(alias="references", default=[]) + + +class Intervention(BaseModel): + + name: str = Field(default=None) + intervention_type: str = Field(alias="type") + + +class ArmsInterventionsModule(BaseModel): + + arms_interventions: list[Intervention] = Field(alias="interventions", default=[]) + + +class Mesh(BaseModel): + + term: str + mesh_id: str = Field(alias="id") + + +class InterventionBrowseModule(BaseModel): + + intervention_meshes: list[Mesh] = Field(alias="meshes", default=[]) + + +class ConditionBrowseModule(BaseModel): + + condition_meshes: list[Mesh] = Field(alias="meshes", default=[]) + + +class ProtocolSection(BaseModel): + + id_module: IDModule = Field(alias="identificationModule") + conditions_module: ConditionsModule = Field(alias="conditionsModule", default=ConditionsModule()) + status_module: StatusModule = Field(alias="statusModule") + design_module: DesignModule = Field(alias="designModule", default=DesignModule()) + references_module: ReferencesModule = Field(alias="referencesModule", default=ReferencesModule()) + arms_interventions_module: ArmsInterventionsModule = Field(alias="armsInterventionsModule", default=ArmsInterventionsModule()) + + +class DerivedSection(BaseModel): + + condition_browse_module: ConditionBrowseModule = Field(alias="conditionBrowseModule", default=ConditionBrowseModule()) + intervention_browse_module: InterventionBrowseModule = Field(alias="interventionBrowseModule", default=InterventionBrowseModule()) + + +class UnflattenedTrial(BaseModel): + """ + Clinicaltrials.gov trial data from REST API response + """ + + protocol_section: ProtocolSection = Field(alias="protocolSection") + derived_section: DerivedSection = Field(alias="derivedSection") diff --git a/src/trial_synth/clinical_trials_dot_gov/store.py b/src/trial_synth/clinical_trials_dot_gov/store.py new file mode 100644 index 0000000..b396295 --- /dev/null +++ b/src/trial_synth/clinical_trials_dot_gov/store.py @@ -0,0 +1,168 @@ +from collections import defaultdict +import csv +import logging +import gzip +from pathlib import Path +import pickle +from typing import Callable + +import pandas as pd +from tqdm import tqdm + +from indra.databases import identifiers + + +logger = logging.getLogger(__name__) + + +class Storer: + + def __init__(self, node_iterator: Callable, node_types: list[str], data_directory: Path): + self.node_iterator = node_iterator + self.node_types = node_types + self.node_types_to_paths = { + node_type: ( + Path(data_directory, f"nodes_{node_type}.tsv.gz"), + Path(data_directory, f"nodes_{node_type}.pkl"), + Path(data_directory, f"nodes_{node_type}_sample.tsv") + ) + for node_type in node_types + } + self.edges_path = Path(data_directory, "edges.tsv.gz") + self.edges_sample_path = Path(data_directory, "edges_sample.tsv") + + def save_data(self, data: pd.DataFrame, path: Path) -> None: + logger.debug(f"Saving Clinicaltrials.gov data to {path}") + try: + data.to_csv(path, sep="\t", index=False, compression="gzip") + except Exception: + logger.exception(f"Could not save data to {path}") + raise + + def save_node_data(self) -> None: + nodes_by_type = defaultdict(list) + # Get all the nodes + nodes = tqdm( + self.node_iterator(), + desc="Node generation", + unit_scale=True, + unit="node", + ) + # Map the nodes to their types + ix = 0 + for ix, node in enumerate(nodes): + nodes_by_type[node.labels[0]].append(node) + if ix == 0: + raise RuntimeError(f"No nodes were generated for {self.name}") + # Get the paths for each type of node and dump the nodes + for node_type in nodes_by_type: + nodes_path, nodes_indra_path, sample_path = self.node_types_to_paths.get(node_type) + nodes = sorted(nodes_by_type[node_type], key=lambda x: (x.db_ns, x.db_id)) + with open(nodes_indra_path, "wb") as fh: + pickle.dump(nodes, fh) + self.dump_nodes_to_path(nodes, nodes_path, sample_path) + + def dump_nodes_to_path( + self, + nodes, + nodes_path, + sample_path=None, + write_mode="wt" + ): + logger.info(f"Dumping node data into {nodes_path}") + if sample_path: + logger.info(f"Dumping sample node data into {sample_path}") + metadata = sorted(set(key for node in nodes for key in node.data)) + header = "id:ID", ":LABEL", *metadata + + node_rows = ( + ( + norm_id(node.db_ns, node.db_id), + ";".join(node.labels), + *[node.data.get(key, "") for key in metadata], + ) + for node in tqdm(nodes, desc="Node serialization", unit_scale=True) + ) + + with gzip.open(nodes_path, mode=write_mode) as node_file: + node_writer = csv.writer(node_file, delimiter="\t") # type: ignore + # Only add header when writing to a new file + if write_mode == "wt": + node_writer.writerow(header) + if sample_path: + with sample_path.open("w") as node_sample_file: + node_sample_writer = csv.writer(node_sample_file, delimiter="\t") + node_sample_writer.writerow(header) + for _, node_row in zip(range(10), node_rows): + node_sample_writer.writerow(node_row) + node_writer.writerow(node_row) + # Write remaining nodes + node_writer.writerows(node_rows) + + + # def dump_edges(self, rels, write_mode="wt"): + def save_edge_data(self, rels, write_mode="wt"): + logger.info(f"Dumping edge data into {self.edges_path}...") + + metadata = sorted(set(key for rel in rels for key in rel.data)) + header = ":START_ID", ":END_ID", ":TYPE", *metadata + + rels = sorted( + rels, key=lambda r: (r.source_ns, r.source_id, r.target_ns, r.target_id) + ) + + edge_rows = ( + ( + norm_id(rel.source_ns, rel.source_id), + norm_id(rel.target_ns, rel.target_id), + rel.rel_type, + *[rel.data.get(key) for key in metadata], + ) + for rel in tqdm(rels, desc="Edges", unit_scale=True) + ) + + with gzip.open(self.edges_path, mode=write_mode) as edge_file: + edge_writer = csv.writer(edge_file, delimiter="\t") # type: ignore + + # Only add header when writing to a new file + if write_mode == "wt": + edge_writer.writerow(header) + if self.edges_sample_path: + with self.edges_sample_path.open("w") as edge_sample_file: + edge_sample_writer = csv.writer(edge_sample_file, delimiter="\t") + edge_sample_writer.writerow(header) + for _, edge_row in zip(range(10), edge_rows): + edge_sample_writer.writerow(edge_row) + edge_writer.writerow(edge_row) + # Write remaining edges + edge_writer.writerows(edge_rows) + + +def norm_id(db_ns, db_id) -> str: + """Normalize an identifier. + + Parameters + ---------- + db_ns : + The namespace of the identifier. + db_id : + The identifier. + + Returns + ------- + : + The normalized identifier. + """ + + identifiers_ns = identifiers.get_identifiers_ns(db_ns) + identifiers_id = db_id + if not identifiers_ns: + identifiers_ns = db_ns.lower() + else: + ns_embedded = identifiers.identifiers_registry.get(identifiers_ns, {}).get( + "namespace_embedded" + ) + if ns_embedded: + identifiers_id = identifiers_id[len(identifiers_ns) + 1 :] + return f"{identifiers_ns}:{identifiers_id}" + diff --git a/src/trial_synth/clinical_trials_dot_gov/transform.py b/src/trial_synth/clinical_trials_dot_gov/transform.py new file mode 100644 index 0000000..d27406b --- /dev/null +++ b/src/trial_synth/clinical_trials_dot_gov/transform.py @@ -0,0 +1,297 @@ +from collections import Counter +import logging +import re +from typing import Iterator + +from addict import Dict +import pandas as pd +from tqdm import tqdm + +import gilda +from indra.databases import mesh_client +from indra.ontology.standardize import standardize_name_db_refs +from indra.statements.agent import get_grounding + + +logger = logging.getLogger(__name__) + + +def or_na(x): + """Return None if x is NaN, otherwise return x""" + return None if pd.isna(x) else x + + +def isna(x): + return True if pd.isna(x) or not x else False + + +def _get_phase(phase_string: str) -> int: + if phase_string and pd.notna(phase_string) and phase_string[-1].isdigit(): + return int(phase_string[-1]) + return -1 + + +def _get_start_year(start_date: str) -> int | None: + if isna(start_date): + return None + match = re.search(r"\d{4}", start_date) + if match: + return int(match[0]) + return None + + +def get_correct_mesh_id(mesh_id, mesh_term=None): + # A proxy for checking whether something is a valid MeSH term is + # to look up its name + name = mesh_client.get_mesh_name(mesh_id, offline=True) + if name: + return mesh_id + # A common issue is with zero padding, where 9 digits are used + # instead of the correct 6, and we can remove the extra zeros + # to get a valid ID + else: + short_id = mesh_id[0] + mesh_id[4:] + name = mesh_client.get_mesh_name(short_id, offline=True) + if name: + return short_id + # Another pattern is one where the MeSH ID is simply invalid but the + # corresponding MeSH term allows us to get a valid ID via reverse + # ID lookup - done here as grounding just to not have to assume + # perfect / up to date naming conventions in the source data. + if mesh_term: + matches = gilda.ground(mesh_term, namespaces=["MESH"]) + if len(matches) == 1: + for k, v in matches[0].get_groundings(): + if k == "MESH": + return v + return None + + +def ground_condition(condition: str) -> list[gilda.grounder.ScoredMatch]: + matches = gilda.ground(condition) + matches = [ + match + for match in matches + if match.term.db in {"MESH", "DOID", "EFO", "HP", "GO"} + ] + if matches: + return matches[0].term + return None + + +def ground_drug(drug: str) -> list[gilda.grounder.ScoredMatch]: + matches = gilda.ground(drug) + if matches: + return matches[0].term + return None + + +def standardize(prefix: str, identifier: str) -> tuple[str, str]: + """Get a standardized prefix and identifier. + + Parameters + ---------- + prefix : + The prefix to standardize. + identifier : + The identifier to standardize. + + Returns + ------- + : + A tuple of the standardized prefix and identifier. + """ + + standard_name, db_refs = standardize_name_db_refs({prefix: identifier}) + db_ns, db_id = get_grounding(db_refs) + if db_ns is None or db_id is None: + return prefix, identifier + return db_ns, db_id + + +class Transformer: + + def __init__(self): + self.has_trial_cond_ns = [] + self.has_trial_cond_id = [] + self.has_trial_nct = [] + self.tested_in_int_ns = [] + self.tested_in_int_id = [] + self.tested_in_nct = [] + + self.problematic_mesh_ids = [] + + self.df = pd.DataFrame() + + def clean_data_values(self) -> None: + """Clean up values in DataFrame""" + + self.df["start_year"] = self.df["StartDate"].apply(_get_start_year).astype("Int64") + + # randomized, Non-Randomized + self.df["randomized"] = self.df["DesignAllocation"].map( + lambda s: "true" if pd.notna(s) and s == "Randomized" else "false" + ) + + # Indicate if the start_year is anticipated or not + self.df["start_year_anticipated"] = self.df["StartDateType"].map( + lambda s: "true" if pd.notna(s) and s == "Anticipated" else "false" + ) + + # Map the phase info for trial to integer (-1 for unknown) + self.df["Phase"] = self.df["Phase"].apply(_get_phase) + + # Create a Neo4j compatible list of references + self.df["ReferencePMID"] = self.df["ReferencePMID"].map( + lambda s: ";".join(f"PUBMED:{pubmed_id}" for pubmed_id in s.split("|")), + na_action="ignore", + ) + + # def get_nodes(self): + def get_nodes(self) -> Iterator: + nctid_to_data = {} + yielded_nodes = set() + for _, row in tqdm(self.df.iterrows(), total=len(self.df)): + nctid_to_data[row["NCTId"]] = { + "study_type": or_na(row["StudyType"]), # observational, interventional + "randomized:boolean": row["randomized"], + "status": or_na(row["OverallStatus"]), # Completed, Active, Recruiting + "phase:int": row["Phase"], + "why_stopped": or_na(row["WhyStopped"]), + "start_year:int": or_na(row["start_year"]), + "start_year_anticipated:boolean": row["start_year_anticipated"], + } + + found_disease_gilda = False + for condition in str(row["Condition"]).split("|"): + cond_term = ground_condition(condition) + if cond_term: + self.has_trial_nct.append(row["NCTId"]) + self.has_trial_cond_ns.append(cond_term.db) + self.has_trial_cond_id.append(cond_term.id) + found_disease_gilda = True + if (cond_term.db, cond_term.id) not in yielded_nodes: + yield Dict( + db_ns=cond_term.db, + db_id=cond_term.id, + labels=["BioEntity"], + data=Dict(name=cond_term.entry_name), + ) + yielded_nodes.add((cond_term.db, cond_term.id)) + if not found_disease_gilda and not isna(row["ConditionMeshId"]): + for mesh_id, mesh_term in zip( + row["ConditionMeshId"].split("|"), + row["ConditionMeshTerm"].split("|") + ): + correct_mesh_id = get_correct_mesh_id(mesh_id, mesh_term) + if not correct_mesh_id: + self.problematic_mesh_ids.append((mesh_id, mesh_term)) + continue + db_ns, db_id = standardize("MESH", correct_mesh_id) + stnd_node = Dict( + db_ns=db_ns, + db_id=db_id, + labels=["BioEntity"], + data={} + ) + node_ns, node_id = stnd_node["db_ns"], stnd_node["db_id"] + self.has_trial_nct.append(row["NCTId"]) + self.has_trial_cond_ns.append(node_ns) + self.has_trial_cond_id.append(node_id) + if (node_ns, node_id) not in yielded_nodes: + yield stnd_node + yielded_nodes.add((node_ns, node_id)) + + # We first try grounding the names with Gilda, if any match, we + # use it, if there are no matches, we go by provided MeSH ID + found_drug_gilda = False + for int_name, int_type in zip( + str(row["InterventionName"]).split("|"), + str(row["InterventionType"]).split("|") + ): + if int_type == "Drug": + drug_term = ground_drug(int_name) + if drug_term: + self.tested_in_int_ns.append(drug_term.db) + self.tested_in_int_id.append(drug_term.id) + self.tested_in_nct.append(row["NCTId"]) + if (drug_term.db, drug_term.id) not in yielded_nodes: + yield Dict( + db_ns=drug_term.db, + db_id=drug_term.id, + labels=["BioEntity"], + data=Dict(name=drug_term.entry_name) + ) + found_drug_gilda = True + yielded_nodes.add((drug_term.db, drug_term.id)) + # If there is no Gilda grounding but there are some MeSH IDs given + if not found_drug_gilda and not isna(row["InterventionMeshId"]): + for mesh_id, mesh_term in zip( + row["InterventionMeshId"].split("|"), + row["InterventionMeshTerm"].split("|"), + ): + correct_mesh_id = get_correct_mesh_id(mesh_id, mesh_term) + if not correct_mesh_id: + self.problematic_mesh_ids.append((mesh_id, mesh_term)) + continue + db_ns, db_id = standardize("MESH", correct_mesh_id) + stnd_node = Dict( + db_ns=db_ns, + db_id=db_id, + labels=["BioEntity"], + data={} + ) + node_ns, node_id = stnd_node["db_ns"], stnd_node["db_id"] + self.tested_in_int_ns.append(node_ns) + self.tested_in_int_id.append(node_id) + self.tested_in_nct.append(row["NCTId"]) + if (node_ns, node_id) not in yielded_nodes: + yield stnd_node + yielded_nodes.add((node_ns, node_id)) + + for nctid in set(self.tested_in_nct) | set(self.has_trial_nct): + if ("CLINICALTRIALS", nctid) not in yielded_nodes: + yield Dict( + db_ns="CLINICALTRIALS", + db_id=nctid, + labels=["ClinicalTrial"], + data=nctid_to_data[nctid], + ) + yielded_nodes.add(("CLINICALTRIALS", nctid)) + + logger.info( + "Problematic MeSH IDs: %s" + % str(Counter(self.problematic_mesh_ids).most_common()) + ) + + def get_edges(self): + added = set() + for cond_ns, cond_id, target_id in zip( + self.has_trial_cond_ns, self.has_trial_cond_id, self.has_trial_nct + ): + if (cond_ns, cond_id, target_id) in added: + continue + added.add((cond_ns, cond_id, target_id)) + yield Dict( + source_ns=cond_ns, + source_id=cond_id, + target_ns="CLINICALTRIALS", + target_id=target_id, + rel_type="has_trial", + data={} + ) + added = set() + for int_ns, int_id, target_id in zip( + self.tested_in_int_ns, self.tested_in_int_id, self.tested_in_nct + ): + if (int_ns, int_id, target_id) in added: + continue + added.add((int_ns, int_id, target_id)) + yield Dict( + source_ns=int_ns, + source_id=int_id, + target_ns="CLINICALTRIALS", + target_id=target_id, + rel_type="tested_in", + data={} + ) diff --git a/src/trial_synth/clinical_trials_dot_gov/util.py b/src/trial_synth/clinical_trials_dot_gov/util.py new file mode 100644 index 0000000..1555a7f --- /dev/null +++ b/src/trial_synth/clinical_trials_dot_gov/util.py @@ -0,0 +1,16 @@ +import logging +from pathlib import Path + +from .config import DATA_DIR + + +logger = logging.getLogger(__name__) + + +def ensure_output_directory_exists(path: Path = DATA_DIR) -> None: + try: + logger.debug(f"Ensuring directory exists: {DATA_DIR}") + DATA_DIR.mkdir(parents=True, exist_ok=True) + except Exception: + logger.exception(f"An error occurred trying to create {DATA_DIR}") + raise diff --git a/src/trial_synth/clinical_trials_dot_gov/validate.py b/src/trial_synth/clinical_trials_dot_gov/validate.py new file mode 100644 index 0000000..8370ebc --- /dev/null +++ b/src/trial_synth/clinical_trials_dot_gov/validate.py @@ -0,0 +1,413 @@ +import logging +from typing import Literal, Any, Optional, Mapping, Iterable + +from indra.statements.validate import assert_valid_db_refs, assert_valid_evidence +from indra.statements import Evidence + + +# See https://neo4j.com/docs/operations-manual/4.4/tools/neo4j-admin/neo4j-admin-import/#import-tool-header-format-properties +# and +# https://neo4j.com/docs/api/python-driver/current/api.html#data-types +# for available data types. +NEO4J_DATA_TYPES = ( + "int", + "long", + "float", + "double", + "boolean", + "byte", + "short", + "char", + "string", + "point", + "date", + "localtime", + "time", + "localdatetime", + "datetime", + "duration", + # Used in node files + "ID", + "LABEL", + # Used in relationship files + "START_ID", + "END_ID", + "TYPE", +) + +DataTypes = Literal[ + "int", + "long", + "float", + "double", + "boolean", + "byte", + "short", + "char", + "string", + "point", + "date", + "localtime", + "time", + "localdatetime", + "datetime", + "duration", + "ID", + "LABEL", + "START_ID", + "END_ID", + "TYPE", +] + + +logger = logging.getLogger(__name__) + + +def get_nodes_by_type(): + nodes = sorted(nodes_by_type[node_type], key=lambda x: (x["db_ns"], x["db_id"])) + + +def validate_node_data(processor_name:str, nodes: list[dict]) -> None: + metadata = sorted(set(key for node in nodes for key in node.data)) + header = "id:ID", ":LABEL", *metadata + + # Validate the headers + try: + validate_headers(header) + except TypeError: + logger.exception(f"Bad node data type in header for {processor_name}") + raise + + # Validate the nodes + try: + validate_nodes(nodes, metadata) + except (UnknownTypeError, DataTypeError): + logger.exception(f"Bad node data type in node data values for {processor_name}") + raise + + +def validate_edge_data(processor_name:str, edges: list[dict]) -> None: + metadata = sorted(set(key for edge in edges for key in edge.data)) + header = ":START_ID", ":END_ID", ":TYPE", *metadata + + try: + validate_headers(header) + except TypeError as e: + logger.error(f"Bad edge data type in header for {self.name}") + raise e + + try: + validate_edges(edges, metadata) + except (UnknownTypeError, DataTypeError) as e: + logger.error(f"Bad edge data type in edge data values for {processor_name}") + raise e + +def assert_valid_node( + db_ns: str, + db_id: str, + data: Optional[Mapping[str, Any]] = None, + check_data: bool = False, +) -> Optional[dict[str, bool]]: + if db_ns == "indra_evidence": + if data and data.get("evidence:string"): + ev = Evidence._from_json(json.loads(data["evidence:string"])) + assert_valid_evidence(ev) + else: + assert_valid_db_refs({db_ns: db_id}) + + if check_data and data: + checked_keys = {} + for key, value in data.items(): + # Skip None values, mark as not checked + if value is None: + checked_keys[key] = False + continue + if ":" in key: + dtype = key.split(":")[1] + else: + # If no data type is specified, string is assumed by Neo4j + dtype = "string" + data_validator(dtype, value) + + checked_keys[key] = True + + return checked_keys + + +def validate_nodes( + nodes: Iterable[dict], + header: Iterable[str], + check_all_data: bool = True, +) -> None: + """Validate the nodes before yielding them. + + Parameters + ---------- + nodes : + The nodes to validate. + header : + The header of the output Neo4j ingest file. + check_all_data : + If True, check all data keys in the nodes. If False, stop checking + when all data keys have been checked. + + Raises + ------ + UnknownTypeError + If a data type is not recognized. + DataTypeError + If a data type does not match the value set in the header. + """ + checked_headers = {key: False for key in header} + for idx, node in enumerate(nodes): + check_data = not all(checked_headers.values()) or check_all_data + try: + checked_fields = assert_valid_node( + node.db_ns, node.db_id, node.data, check_data + ) + if checked_fields: + for key, checked in checked_fields.items(): + if checked: + checked_headers[key] = True + + # Check if this was the iteration when all headers were checked + if check_data and all(checked_headers.values()) and not check_all_data: + logger.info(f"All node data keys checked at index {idx}. " + f"Skipping the rest") + + except (UnknownTypeError, DataTypeError) as e: + logger.error(f"{idx}: {node} - {e}") + logger.error("Bad node data type(s) detected") + raise e + except Exception as e: + logger.info(f"{idx}: {node} - {e}") + continue + + +def validate_edges( + relations: Iterable[dict], + header: Iterable[str], + check_all_data: bool = True +) -> None: + """Validate the relations before yielding them. + + Parameters + ---------- + relations : + The relations to validate. + header : + The header of the output Neo4j ingest file. + check_all_data : + If True, check all data keys in the relations. If False, stop checking + when all data keys have been checked. + + Raises + ------ + UnknownTypeError + If a data type is not recognized. + DataTypeError + If a data type does not match the value set in the header. + """ + + checked_headers = {key: False for key in header} + for idx, rel in enumerate(relations): + try: + check_data = not all(checked_headers.values()) or check_all_data + checked_fields = assert_valid_node( + rel.source_ns, rel.source_id, rel.data, check_data + ) + assert_valid_node(rel.target_ns, rel.target_id) + if checked_fields: + for key, checked in checked_fields.items(): + if checked: + checked_headers[key] = True + + # Check if this was the iteration when all headers were checked + if check_data and all(checked_headers.values()) and not check_all_data: + logger.info(f"All relation data keys checked at index {idx}. " + f"Skipping the rest") + yield rel + except (UnknownTypeError, DataTypeError) as e: + logger.error(f"{idx}: {rel} - {e}") + logger.error("Bad relation data type(s) detected") + raise e + except Exception as e: + logger.info(f"{idx}: {rel} - {e}") + continue + + +def validate_headers(headers: Iterable[str]) -> None: + """Check for data types in the headers""" + for header in headers: + # If : is in the header and there is something after it check if + # it's a valid data type + if ":" in header and header.split(":")[1]: + dtype = header.split(":")[1] + + # Strip trailing '[]' for array types + if dtype.endswith("[]"): + dtype = dtype[:-2] + + if dtype not in NEO4J_DATA_TYPES: + raise TypeError( + f"Invalid header data type '{dtype}' for header {header}" + ) + + +class DataTypeError(TypeError): + """Raised when a data value is not of the expected type""" + + +class UnknownTypeError(TypeError): + """Raised when a data type is not recognized.""" + + +def data_validator(data_type: str, value: Any): + """Validate that the data type matches the value. + + Parameters + ---------- + data_type : + The Neo4j data type to validate against. + value : + The value to validate. + + Raises + ------ + DataTypeError + If the value does not validate against the Neo4j data type. + UnknownTypeError + If data_type is not recognized as a Neo4j data type. + """ + # None's are provided in the data dictionaries upon initial + # node/relationship generation as a missing/null value. Once dumped, + # the None's are converted to empty strings which is read in when nodes + # are assembled. If we encounter a null value, there is no need to + # validate it. + null_data = {None, ""} + if value in null_data: + return + + if isinstance(value, str): + value_list = value.split(";") if data_type.endswith("[]") else [value] + else: + value_list = [value] + value_list = [val for val in value_list if val not in null_data] + if not value_list: + return + data_type = data_type.rstrip("[]") + if data_type == "int" or data_type == "long" or data_type == "short": + for val in value_list: + if isinstance(val, str): + # Try to convert to int + try: + val = int(val) + except ValueError as e: + raise DataTypeError( + f"Data value '{val}' is of the wrong type to conform " + f"with Neo4j type {data_type}. Expected a value of " + f"type int, but got value of type str with value " + f"'{val}' instead." + ) from e + if not isinstance(val, int): + raise DataTypeError( + f"Data value '{val}' is of the wrong type to conform with " + f"Neo4j type {data_type}. Expected a value of type int, " + f"but got value of type {type(val)} instead." + ) + elif data_type == "float" or data_type == "double": + for val in value_list: + if isinstance(val, str): + # Try to convert to float + try: + val = float(val) + except ValueError as e: + raise DataTypeError( + f"Data value '{val}' is of the wrong type to conform " + f"with Neo4j type {data_type}. Expected a value of " + f"type float, but got value of type str with value " + f"'{val}' instead." + ) from e + if not isinstance(val, float): + raise DataTypeError( + f"Data value '{val}' is of the wrong type to conform with " + f"Neo4j type {data_type}. Expected a value of type float, " + f"but got value of type {type(val)} instead." + ) + elif data_type == "boolean": + for val in value_list: + if not isinstance(val, str) or val not in ("true", "false"): + raise DataTypeError( + f"Data value '{val}' is of the wrong type to conform with " + f"Neo4j type {data_type}. Expected a value of type str " + f"with literal value 'true' or 'false', but got value of " + f"type {type(val)} with value '{val}' instead." + ) + elif data_type == "byte": + for val in value_list: + if not isinstance(val, (bytes, int)): + raise DataTypeError( + f"Data value '{val}' is of the wrong type to conform with " + f"Neo4j type {data_type}. Expected a value of type bytes " + f"or int, but got value of type {type(val)} instead." + ) + elif data_type == "char": + for val in value_list: + if not isinstance(val, str): + raise DataTypeError( + f"Data value '{val}' is of the wrong type to conform with " + f"Neo4j type {data_type}. Expected a value of type str, " + f"but got value of type {type(val)} instead." + ) + elif data_type == "string": + for val in value_list: + # Catch string representations of numbers + if isinstance(val, (int, float)): + try: + val = str(val) + except ValueError as e: + raise DataTypeError( + f"Data value '{val}' is of the wrong type to conform " + f"with Neo4j type {data_type}. Expected a value of " + f"type str, int or float, but got value of type " + f"{type(val)} instead." + ) from e + if not isinstance(val, str): + raise DataTypeError( + f"Data value '{val}' is of the wrong type to conform with " + f"Neo4j type {data_type}. Expected a value of type str, " + f"int or float, but got value of type {type(val)} instead." + ) + elif data_type == "point": + raise NotImplementedError( + "Neo4j point data type validation is not implemented" + ) + # Todo: make stricter validation for dates and times: + # https://neo4j.com/docs/cypher-manual/4.4/syntax/temporal/#cypher-temporal-instants + elif data_type in [ + "date", + "localtime", + "time", + "localdatetime", + "datetime", + "duration", + ]: + for val in value_list: + if not isinstance(val, (str, int)): + raise DataTypeError( + f"Data value '{val}' is of the wrong type to conform with " + f"Neo4j type {data_type}. Expected a value of type str " + f"or int, but got value of type {type(val)} instead." + ) + elif data_type in ["ID", "LABEL", "START_ID", "END_ID", "TYPE"]: + for val in value_list: + if not isinstance(val, (str, int)): + raise DataTypeError( + f"Data value '{val}' is of the wrong type to conform with " + f"Neo4j type {data_type}. Expected a value of type str " + f"or int, but got value of type {type(val)} instead." + ) + else: + raise UnknownTypeError( + f"{data_type} is not recognized as a Neo4j data type." + ) diff --git a/tests/test_clinical_trials_dot_gov.py b/tests/test_clinical_trials_dot_gov.py new file mode 100644 index 0000000..3903695 --- /dev/null +++ b/tests/test_clinical_trials_dot_gov.py @@ -0,0 +1,29 @@ +import os + +from pydantic import ValidationError +import pytest + +from trial_synth.clinical_trials_dot_gov import ( + config, + fetch +) + + +DOCKERIZED = os.environ.get("DOCKERIZED", False) + + +@pytest.mark.skipif(DOCKERIZED, reason="Test against API, not stub") +def test_stability(): + """ + If the structure of the API response changes it will break the pipeline. + Get sample data from the live API and validate it using its data model. + """ + + configuration = config.Config() + json_data = fetch.send_request(configuration.api_url, configuration.api_parameters) + studies = json_data.get("studies", []) + try: + _ = fetch.flatten_data(studies) + except ValidationError as exc: + pytest.fail(f"Unexpected error while flattening API response data: {exc}") +